Now that the 2020 election is officially over and Biden was elected as the President of the United States, it is important that I reflect on my prediction model. I am excited to see how I cold learn from my model for future models that I create.
Let’s first recap on my prediction model to get a better picture of what it was.
My prediction model was an ensemble model that predicted the popular vote share for each state .
Given that the Time For Change Model was an inspiration, I decided to focus my model on historical republican vote share as Trump was the incumbent for the 2020 election and incumbency was one predictor in the Time For Change Model.
I decided to separate America into three categories - red states, blue states, and battleground states - for my model to adjust for overfitting. The grouping were based on how FiveThirtyEight grouped states.
My model used the following data:
In my model, I decided to classify approval, Q2 GDP growth, and turnout as fundamentals
Thus, my ensemble model weighted the poll model (using only polls) by 0.96 and the fundamental model (using only fundamentals) by 0.04 as I weighted the model based on FiveThirtyEight’s reasoning that polls are better predictors as the election nears since fundamentals become more noisy instead.
My final prediction using the ensemble model was that Biden was projected to win 310 electoral votes while Trump is projected to win 228 votes, meaning Biden would become president-elect of the United States.